code: def test_given_parameterized_estimation_func_when_confidence_interval_then_can_use_bootstrap_sampling_to_bind_parameters():
      i = 0.0
  
      def parameterized_counter(some_parameter):
          nonlocal i
          i += 1.0
          return {some_parameter: i}
  
      median, interval = confidence_intervals(
          bootstrap_sampling(parameterized_counter, "A"),
          num_bootstrap_resamples=20,
      )
  
      assert median["A"] == pytest.approx(10.5)
      assert np.allclose(interval["A"], [1.95, 19.05])
  
file_path: dowhy/tests/gcm/test_confidence_intervals_cms.py
imports: import networkx
  import numpy
  import pandas
  import pytest
  from flaky import flaky
  from dowhy.gcm import AdditiveNoiseModel, EmpiricalDistribution, ProbabilisticCausalModel, bootstrap_sampling, draw_samples, fit_and_compute
  from dowhy.gcm.confidence_intervals import confidence_intervals
  from dowhy.gcm.ml import create_hist_gradient_boost_regressor
label_desc: Use image node_id 2 for calling a global function with example usage: test_given_parameterized_estimation_func_when_confidence_interval_then_can_use_bootstrap_sampling_to_bind_parameters() and returns: dict